Unique Relationship Between Optimum Compaction Properties of Fine-Grained Soils Across Rational Compactive Efforts: A Validation Study
Bibliographic record
Abstract
Abstract Among the many proposals for estimating the compaction characteristics of fine-grained soils for different compaction energy levels (CELs), energy conversion (EC) models are gaining increased attention. These models work on the premise of employing measured optimum moisture content (OMC) and maximum dry unit weight (MDUW) values obtained for a rational CEL (e.g., standard or reduced-standard Proctor (SP or RSP)) to predict the same for other CELs. This study revisits the most recently proposed EC-based compaction modeling framework, critically examining its asserted accuracy and hence identifying its true potentials. This was achieved by performing comprehensive statistical analyses on a newly compiled database of 206 compaction test results, entailing 70 different fine-grained soils (with liquid limits ranging 19–365%) and accounting for CELs of 202.0–2723.5 kJ/m 3 . It was demonstrated that 99% and 96% of the differences between the SP-converted and measured values for OMC and MDUW, respectively, fall within the allowable margins of OMC and MDUW measurement errors permitted by the Australian AS 1289.5.1.1/AS 1289.5.2.1 standards (satisfying their recommended ≥ 95% requirement). Equally favorable results were also obtained for the RSP-based conversions. These findings reaffirmed that the optimum compaction parameters across rational CELs are somewhat uniquely related, and the effects of fine-grained soil attributes on soil compactability are adequately captured/explained by the measured OMC and MDUW values employed as the conversion inputs/predictors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".